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Raymond Hettinger

@raymondh

11 Nov 21

#Python's structural pattern matching is new, so the best practices aren't yet known.

Just added to my personal list: Add a comment or assertion noting when case ordering is important.

Otherwise, a future maintainer will be bitten by the illusion of case independence.

1 of 4

match x:
case bool():
...
case int():
assert not isinstance(x, bool)
...
case Counter():
...
case dict():
assert not isinstance(x, Counter)
case _:
raise TypeError

2 of 4

match sales_report:
case {'dept': dept, 'holiday': event}:
# Holiday case must precede other cases
...
case {'dept': 'shoes'}:
...
case {'dept': 'hats'}:
...

3 of 4

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Raymond Hettinger

@raymondh

9 Nov 21

#Python tip: Structural pattern matching works with abstract base classes such as: Contains, Hashable, Iterable, Iterator, Reversible, Generator, Sized, Callable, and Collection.

match obj:
case Hashable:
...

Matching a collection ABC is preferable to looking for the required methods directly.

The complication is that those methods may be present but could be set to None.

The ABCs listed above handle the None checks for you.

For more complex ABCs, such as Sequence and Mapping, the presence of the required methods is insufficient.

The object's class needs to either inherit from the ABC or be registered with the ABC.

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Raymond Hettinger

@raymondh

6 Apr 21

#Python factlet: The dict.popitem() method is guaranteed to remove key/value pairs in LIFO order.

>>> d = dict(red=1, green=2, blue=3)
>>> d.popitem()
('blue', 3)
>>> d.popitem()
('green', 2)
>>> d.popitem()
('red', 1)

1/

In contrast, OrderedDict.popitem() supports both FIFO and LIFO extraction of key/value pairs.

>>> from collections import OrderedDict
>>> d = OrderedDict(red=1, green=2, blue=3)

>>> d.popitem(last=False) # FIFO
('red', 1)

>>> d.popitem() # LIFO
('blue', 3)

2/

OrderedDict can efficiently move entries to either end without a hash table update.

>>> d = OrderedDict(red=1, green=2, blue=3)

>>> d.move_to_end('green')
>>> list(d)
['red', 'blue', 'green']

>>> d.move_to_end('green', last=False)
>>> list(d)
['green', 'red', 'blue']

3/

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Raymond Hettinger

@raymondh

3 Jan 21

#Python factlet: The len() function insists that the corresponding __len__() method return a value x such that:

0 ≤ x.__index__() ≤ sys.maxsize

* 3.0 and '3' don't have an __index__ method.
* -1 is too small.
* sys.maxsize+1 is too big.

1/

You could call __len__() successfully, but the len() function fails:

class A:
def __len__(self):
return -1

>>> a = A()

>>> a.__len__()
-1

>>> len(a)
...
ValueError: __len__() should return >= 0

2/

Classes written in C typically implement __len__() with the mp_length or sq_length slot. That constrains them to sys.maxsize limits:

>>> r = range(10**100)
>>> r.__len__()
Traceback (most recent call last):
...
OverflowError: Python int too large to convert to C ssize_t

3/

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Raymond Hettinger

@raymondh

2 Jan 21

@yera_ee

@yera_ee Each way has its advantages.

With dataclasses, you get nice attribute access, error checking, a name for the aggregate data, and a more restrictive equality test. All good things.

Dicts are at the core of the language and are interoperable with many other tools: json, **kw, …

@yera_ee

@yera_ee Dicts have a rich assortment of methods and operators.
People learn to use dicts on their first day.
Many existing tools accept or return dicts.
pprint() knows how to handle dicts.
Dicts are super fast.
JSON.
Dicts underlie many other tools.

@yera_ee

@yera_ee Embrace dataclasses but don't develop an aversion to dicts.

Python is a very dict centric language.

Mentally rejecting dicts would be like developing an allergy to the language itself. It leads to fighting the language rather than working in harmony with it.

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Raymond Hettinger

@raymondh

27 Dec 20

1/ #Python tip: Override the signature for *args with the __text_signature__ attribute:

def randrange(*args):
'Choose a random value from range(start[, stop[, step]]).'
return random.choice(range(*args))

randrange.__text_signature__ = '(start, stop, step, /)'

2/ The attribute is accessed by the inspect module:

>>> inspect.signature(randrange)
<Signature (start, stop, step, /)>

3/ Tooltips and help() will now be more informative:

>>> help(randrange)
randrange(start, stop, step, /)
Choose a random value from range(start[, stop[, step]]).

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